Open jbe456 opened 4 years ago
A few extra comments from Georges/Anastasia:
The authors of the article discuss a way to improve the calibration quality of a neural network applied to pricing and valuation problems, by including not only the risk factors and expected result into the training set, but also including the derivatives of the expected result to risk factors.
Because they have successfully implemented AAD at Danske and are proud of the fact that they can produce greeks as needed, at a low cost.
For instance, imagine we are trying to train an ML model to compute FRTB. We'd generate a set of portfolios and expected results to calibrate the model. Having trained a model, we can send any portfolio to the network - and it will compute FRTB. If we could also compute derivatives of FRTB to inputs - then the network will be able to extract factors more efficiently, and calibrate better.
Youtube on AAD:
Let's explore these two applications for atoti:
Machine-learning application for atoti: Demonstrate that a post processor can fetch ML model parameters from a TensorFlow calibrated model (for example, a linear classifier), and apply it to the data -> for further aggregation. For example, churn rate/customer classification for a telecommunication company. https://www.bedrockdata.com/blog/introduction-to-ai-machine-learning-part-iii We can subscribe and update model parameters in real-time mode! :) This is captured by https://github.com/atoti/notebooks/issues/35
Implement some sort of AAD as an algorithm: AAD can produce Greeks at lower cost and therefore open the possibility of
Description
Explore how Atoti can leverage risk management with AAD and ML
Resources
https://antoinesavine.com/2020/05/04/differential-machine-learning/